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################################################################################
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# Simple unit tests for MPNNs.
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import numpy as np
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import tensorflow as tf
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import mpnn
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def build_feed_dict(ph, h, adjacency, dist, m):
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return {ph[0]: h, ph[1]: adjacency, ph[2]: dist, ph[3]: m}
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def get_permutation_test_outputs(hparams):
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num_nodes = 4
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batch_size = 3
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input_dim = 5
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output_dim = 2
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with tf.Graph().as_default():
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model = mpnn.MPNN(hparams, input_dim, output_dim, num_edge_class=5)
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ph, _ = model.get_fprop_placeholders()
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pred_op = model.fprop(*ph)
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adjacency = np.random.randint(2, size=(batch_size, num_nodes, num_nodes))
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dist = np.random.rand(batch_size, num_nodes, num_nodes)
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h = np.random.rand(batch_size, num_nodes, input_dim)
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perm = np.random.permutation(num_nodes)
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h_perm = np.zeros_like(h)
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adjacency_perm = np.zeros_like(adjacency)
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dist_perm = np.zeros_like(dist)
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m = np.full((batch_size, num_nodes), 1)
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for i in xrange(len(h_perm)):
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h_perm[i] = h[i][perm]
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for i in xrange(len(adjacency_perm)):
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adjacency_perm[i] = adjacency[i][perm]
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dist_perm[i] = dist[i][perm]
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for j in xrange(len(adjacency_perm[i])):
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adjacency_perm[i][j] = adjacency_perm[i][j][perm]
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dist_perm[i][j] = dist_perm[i][j][perm]
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print h.shape, h_perm.shape
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print adjacency.shape, adjacency_perm.shape
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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output = sess.run(
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pred_op, feed_dict=build_feed_dict(ph, h, adjacency, dist, m))
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output_perm = sess.run(
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pred_op,
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feed_dict=build_feed_dict(ph, h_perm, adjacency_perm, dist_perm, m))
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print "output no perm:"
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print output
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print "\noutput perm:"
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print output_perm
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return output, output_perm
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def get_pad_test_outputs(hparams):
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# TODO(gilmer) This should test different paddings within the same batch,
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# in a similar way as in set2vec_test.py
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hparams = mpnn.MPNN.default_hparams()
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num_nodes = 4
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batch_size = 3
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input_dim = 5
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output_dim = 2
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pad = 3
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with tf.Graph().as_default():
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model = mpnn.MPNN(hparams, input_dim, output_dim, num_edge_class=5)
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ph, _ = model.get_fprop_placeholders()
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pred_op = model.fprop(*ph)
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adjacency = np.random.randint(2, size=(batch_size, num_nodes, num_nodes))
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dist = np.random.rand(batch_size, num_nodes, num_nodes)
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h = np.random.rand(batch_size, num_nodes, input_dim)
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m = np.full((batch_size, num_nodes), 1.0)
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h_pad = np.zeros((h.shape[0], h.shape[1] + pad, h.shape[2]))
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adjacency_pad = np.zeros((adjacency.shape[0], adjacency.shape[1] + pad,
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adjacency.shape[2] + pad))
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dist_pad = np.zeros((dist.shape[0], dist.shape[1] + pad,
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dist.shape[2] + pad))
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m_pad = np.zeros((batch_size, num_nodes + pad))
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for i in xrange(batch_size):
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for j in xrange(num_nodes):
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m_pad[i][j] = 1
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for i in xrange(len(h)):
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for j in xrange(len(h[i])):
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for k in xrange(len(h[i][j])):
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h_pad[i][j][k] = h[i][j][k]
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for i in xrange(len(adjacency)):
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for j in xrange(len(adjacency[i])):
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